The Training Data Is a Liability You Cannot See

Most artificial intelligence systems carry a hidden liability: nobody can prove what data trained them. I argue that data provenance and poisoning are the real exposure, that you cannot defend outputs you cannot trace to inputs, and that the only honest answer is a signed, hash-chained record you can verify offline…

https://mickai.co.uk/articles/training-data-is-a-liability-you-cannot-see

#dataprovenance #datapoisoning #artificialintelligencesecurity #modelauditing #postquantumcryptography

A new auditing framework evaluates label-privacy leakage in ML models without modifying training data. By testing how well an attacker distinguishes between training labels and proxy labels, researchers showed that stronger privacy settings greatly reduce leakage signals.
Consistent results across datasets suggest this could lower the operational barriers to ML privacy testing.

What’s your view - is this a step forward for practical ML security?

Source: https://www.helpnetsecurity.com/2025/11/28/machine-learning-privacy-audit-checks/

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#AIsecurity #MachineLearning #DataPrivacy #CyberSecurity #ModelAuditing #ResponsibleAI #SecurityResearch #MLTools